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Similar coefficient for cluster of probability density functions

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  • Tai VoVan
  • Thao NguyenTrang

Abstract

In this article, we propose a new criterion to evaluate the similarity of probability density functions (pdfs). We call this the criterion on similar coefficient of cluster (SCC) and use it as a tool to deal with overlap coefficients of pdfs in normal standard on [0;1]. With the support of the self-update algorithm for determining the suitable number of clusters, SCC then becomes a criterion to establish the corresponding cluster for pdfs. Moreover, some results on determination of SCC in case of two and more than two pdfs as well as relations of different SCCs and other measures are presented. The numerical examples in both synthetic data and real data are given not only to illustrate the suitability of proposed theories and algorithms but also to demonstrate the applicability and innovation of the proposed algorithm.

Suggested Citation

  • Tai VoVan & Thao NguyenTrang, 2018. "Similar coefficient for cluster of probability density functions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(8), pages 1792-1811, April.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:8:p:1792-1811
    DOI: 10.1080/03610926.2017.1327075
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    Cited by:

    1. Tai Vovan & Dinh Phamtoan & Le Hoang Tuan & Thao Nguyentrang, 2021. "An automatic clustering for interval data using the genetic algorithm," Annals of Operations Research, Springer, vol. 303(1), pages 359-380, August.

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